Domain adaptation:
Domain adaptation and transfer learning techniques have been applied to scenarios where a single agent learns visual categories (monitor, keyboard) from one domain (Amazon product images) but would like to apply its models to a different domain (webcam images) [1]}, [2]}, [3]}, [4]}. Our setting is different. The two agents learning the visual categories will be using their models in their own domains. There is no “adaptation” required. Instead, the goal is for the two agents to communicate their current knowledge with each other so as to make them both more accurate in their own respective domains. The agents continue to operate – i.e., be used or “tested” – in their own environments and domains. We assume that while the source of data and choice of sensors is different in both domains, the semantic distribution of categories – the attribute-category relationships – are similar in both domains (zebras in both domains are striped and have four legs). This is a reasonable assumption in many realistic scenarios (robots in home environments). If one agent was learning about bears in a region where brown bears are common, and the other was learning about bears where white polar bears are common, this assumption would be violated. Finally, domain adaptation approaches typically rely on large quantities of labeled data from one of the domains (the source domain). In our setting, both domains use only a few labeled images.
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